High-definition Spatial Metabolomics Method For Tissue Micro-region Resolution And Its Application

In recent years,the development of omics techniques has greatly advanced the understanding of life activities and disease mechanisms and clinical precision diagnosis and treatment.However,omics analysis techniques based on tissue homogenization or single cell dissociation can disrupt the spatial location of cells and molecules in tissues,and cannot obtain information on the distribution and interactions of molecules and cells in highly heterogeneous tissues(e.g.,brain,tumor,etc.).In response to these problems,spatial metabolomics(SM)based on mass spectrometry imaging(MSI)technology has been rapidly developed and shows great potential in exploring pathogenesis and discovering biomarkers.Spatial resolution is one of the important parameters of MSI,and with the continuous development of the technology,MSI with micron-level resolution has enabled the precise localization of metabolites in cells.However,sensitivity and spatial resolution are inversely related,and a good trade-off between sensitivity and resolution has been a pressing issue in MSI analysis.In this study,based on the air flow-assisted desorption electrospray ionization mass spectrometry imaging(AFADESI-MSI)developed by the group,we designed a fine spray probe and systematically optimized and improved the key parameters of imaging to develop a high-definition spatially resolved metabolomics method for tissues with complex micro-regions,ensuring the detection sensitivity while achieving a spatial resolution of 30 μm.We have achieved high-definition spatially resolved metabolomics analysis of mouse brain and clinical gastric cancer tissues,and constructed spatial metabolic profiles and metabolic networks.Based on the spatial metabolomics technology developed by the group,the group further integrated spatial lipidomics and spatial transcriptomics technologies to achieve in situ accurate joint analysis of metabolome,lipidome and transcriptome data in tumor tissue micro-regions on adjacent sections of the same tumor tissue sample.In situ characterization of metabolic regulation and interactions among tumor cells,immune cells and stromal cells in the tumor tissue microenvironment was achieved by identifying cell types and constructing association networks of metabolites,lipids and upstream regulatory genes.The research of this dissertation consists of three main parts as follows:1.Study on high-definition spatial-resolution metabolomics methodTo address the problem of low sensitivity of high spatial resolution mass spectrometry imaging for small molecule metabolite detection,a high resolution,high sensitivity and high coverage spatial metabolomics method based on AFADESI-MSI was investigated and developed.Based on the principle of spray desorption,a fine probe with capillary inner diameter of 20 μm was designed,and key parameters such as probe position,spray solvent composition and spray solvent flow rate were investigated and optimized.By applying the scanning strategy of point-by-point stripping,the spatial resolution was effectively improved by complete desorption and reduced moving step,and the sensitivity of detection was ensured while obtaining reasonable resolution,and stable imaging results were obtained.Applying this probe and scanning strategy,a high spatial resolution metabolomic analysis of the mouse cerebellum at 30 μm was achieved,and the white matter,gray matter and granular layer of the cerebellum were clearly visible in MSI.The ion signal intensity was reduced by only less than 1/2 in terms of the pixel point size reduction ratio(～6.25 times).and the ion intensity per unit area was improved.In addition,the dynamic detection range of the method covered 3-4 orders of magnitude,and a total of 406 species-rich metabolites,including cholines,polyamines,carnitines,amino acids,nucleosides,organic acids,carbohydrates and lipids,were annotated in both positive and negative ion modes.The high quality images and data obtained indicate that the method is stable and reliable,meets the analytical requirements of metabolomics,and shows unique advantages for the analysis of small molecules metabolites with low content in tissues with fine structure.2.Application of a high spatial resolution metabolomics approach to construct a metabolic profile of the mouse brainA comprehensive high spatial resolution metabolomic analysis of heterogeneous mouse brain sections was performed to map the high definition spatial metabolic profile of mouse brain using the above method.MSI obtained metabolite specific distribution characteristics in 15 functional brain microregions,and a spatial resolution of 30 μm localized metabolites to substructures in the hippocampus.Further,the MSI data were matched with the metabolite database established by applying LC/GC-MS analysis,and a total of 333 and 162 metabolite ions were annotated in positive and negative ion modes.Correlation analysis showed that the metabolic phenotypes of brain regions largely followed the classical anatomical division of structure and function,and adjacent or identical types of structures were highly correlated at the metabolic molecular level.Correlating metabolites and brain functions,the distribution characteristics of most of them were found to be closely related to the functions of their regions.Combined with the metabolic pathway enrichment analysis.the spatial metabolic network of mouse brain was successfully mapped.The relevant metabolite distribution characteristics and upstream and downstream information of metabolic pathways will help to explain the complex regulatory network of the brain and provide a comprehensive metabolic reference for regional function and homeostasis in the brain.3.Application of a high spatial resolution metabolomics approach to characterize the metabolic alterations of immune cells in the tumor microenvironmentA high spatial resolution metabolomics approach was applied to visualize the postoperative samples of clinical gastric cancer,focusing on the metabolic characteristics of lymphoid follicles at the junction of tumor and normal tissues to characterize the metabolic reprogramming of immune cells.A total of 348 differential metabolites were screened in normal tissues and lymphoid follicles by precise extraction of metabolite information from microregions and multivariate statistical analysis,and further clustering analysis showed that lymphoid follicles in different microenvironments had different metabolic profiles.The metabolic pathway enrichment analysis of the annotated differential metabolites showed that lymphoid follicles were significantly altered in energy metabolism,amino acid metabolism and lipid metabolism.The more pronounced metabolic alterations in pentose phosphate pathway metabolism in immune cells near the tumor suggest that tumor cells interact with immune cells through energy metabolic pathways and that immune cells undergo metabolic reprogramming during tumor invasion.In addition,metabolic reprogramming-related metabolites such as histamine,glutamine.lactate and spermine were enriched in immune cells to varying degrees,which may have suppressed their immune function.4.Spatial multi-omics techniques reveal cell-specific metabolic reprogramming and interactions associated with gastric cancerTumors are complex tissues composed of many different cell types.To explore the interaction and metabolic reprogramming between immune cells and tumor cells,the study combined mass spectrometry imaging-based spatial metabolomics.spatial lipidomics,and sequencing-based spatial transcriptomics approaches to perform spatial multi-omics analysis of human gastric cancer tissues.The imaging maps obtained by the three methods were aligned by coordinate annotation of sampling points,linking metabolite,lipid and gene expression information to characterize tumor metabolic reprogramming at multiple levels through data mining.The results of the multi-omics analysis showed that metabolic reprogramming occurred in arginine and proline metabolism,glutamate and glutamine metabolism,lipid and fatty acid metabolism in gastric cancer,and there were significant differences in the expression of their associated genes.In addition,comprehensive data from a spatial multi-omics approach identified cell types and distributions in the complex tumor microenvironment and revealed a distinct transcriptional profile and significant immunometabolic alterations in the "tumor-normal interface" region,which is rich in immune cells.The study provides an atlas of metabolite,lipid and gene expression patterns,including tumor cells and surrounding normal cells,which will help to gain insight into biochemical heterogeneity within tumors and decipher the role of metabolism in cancer development.